Wei Yanbo, Lu Zhizhong, Yuan Gannan, Fang Zhao, Huang Yu
College of Automation, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China.
College of Science, Harbin Engineering University, No. 145 Nantong Street, Harbin 150001, China.
Sensors (Basel). 2017 May 13;17(5):1120. doi: 10.3390/s17051120.
In this paper, the application of the emerging compressed sensing (CS) theory and the geometric characteristics of the targets in radar images are investigated. Currently, the signal detection algorithms based on the CS theory require knowing the prior knowledge of the sparsity of target signals. However, in practice, it is often impossible to know the sparsity in advance. To solve this problem, a novel sparsity adaptive matching pursuit (SAMP) detection algorithm is proposed. This algorithm executes the detection task by updating the support set and gradually increasing the sparsity to approximate the original signal. To verify the effectiveness of the proposed algorithm, the data collected in 2010 at Pingtan, which located on the coast of the East China Sea, were applied. Experiment results illustrate that the proposed method adaptively completes the detection task without knowing the signal sparsity, and the similar detection performance is close to the matching pursuit (MP) and orthogonal matching pursuit (OMP) detection algorithms.
本文研究了新兴的压缩感知（CS）理论的应用以及雷达图像中目标的几何特征。目前，基于CS理论的信号检测算法需要知道目标信号稀疏性的先验知识。然而，在实际中，往往无法提前知道稀疏性。为了解决这个问题，提出了一种新颖的稀疏性自适应匹配追踪（SAMP）检测算法。该算法通过更新支撑集并逐渐增加稀疏性来逼近原始信号，从而执行检测任务。为了验证所提算法的有效性，应用了2010年在位于中国东海海岸的平潭收集的数据。实验结果表明，所提方法在不知道信号稀疏性的情况下自适应地完成检测任务，并且其相似检测性能接近匹配追踪（MP）和正交匹配追踪（OMP）检测算法。
